Abstract

Walking is the most common activity among people
who are physically active. Standard practice physical activity
characterization from body-mounted inertial sensors involves
using accelerometer-generated counts. There are two problems
with this - imprecision and incompleteness. The first, imprecision,
is because commercial accelerometers use proprietary methods
to convert linear accelerations into counts. The second, incompleteness,
is because human body movement consists of both
linear accelerations and angular rotations - the latter is ignored
in current count-based approaches. We address both these problems
by directly predicting energy expenditure using streaming
data from a hip-mounted inertial sensor comprised of a triaxial
accelerometer and a tri-axial gyroscope during steady-state
treadmill walking (as measured by rate of oxygen consumption
- V O2, mL/min). We use a linear probabilistic model, Bayesian
Linear Regression (BLR), to predict energy expenditure based
on modelling joint probabilities of the streaming data. The prediction
is significantly better (p<0.05 per subject using ANOVA)
when streaming data from all 6 axes (3 linear accelerations and 3
rotational velocities) are compared (prediction error ~ 35 ml/min)
with streaming data from only 2 linear accelerations (prediction
error ~ 85 ml/min) as is common in current practice. We also
show how counts from a commercially available accelerometer,
the Actigraph GT1M, can be reproduced from raw streaming
acceleration data (up to a linear transformation). This is done by
defining a feature vector to characterize energy expenditure and
showing that this feature vector is strongly correlated (.9787
.0089 for the X-axis and .9141 .0460 for the Y-axis acceleration
streams) to Actigraph generated counts. The paper emphasizes
the role of probabilistic techniques in conjunction with joint
modeling of tri-axial accelerations and rotational rates to improve
energy expenditure prediction for steady-state treadmill walking.